





"""simple docstring"""


import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__magic_name__       :					List[str]						=			{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if got_ver is None or want_ver is None:
												raise ValueError(
												    f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
												    f""" reinstalling {pkg}."""     )
						if not ops[op](version.parse(SCREAMING_SNAKE_CASE     )							,     version.parse(SCREAMING_SNAKE_CASE     )     ):
												raise ImportError(
												    f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = None     ):
						UpperCamelCase				:		str				   =			f"""\n{hint}""" if hint is not None else """"""

						# non-versioned check
						if re.match(r"""^[\w_\-\d]+$"""							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			requirement, None, None
						else:
												UpperCamelCase				:		str				   =			re.findall(r"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""							,     SCREAMING_SNAKE_CASE     )
												if not match:
																		raise ValueError(
																		    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
																		    f""" got {requirement}"""     )
												UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			match[0]
												UpperCamelCase				:		List[str]				   =			want_full.split(""","""     )  # there could be multiple requirements
												UpperCamelCase				:		List[Any]				   =			{}
												for w in want_range:
																		UpperCamelCase				:		Dict				   =			re.findall(r"""^([\s!=<>]{1,2})(.+)"""							,     SCREAMING_SNAKE_CASE     )
																		if not match:
																								raise ValueError(
																								    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
																								    f""" but got {requirement}"""     )
																		UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			match[0]
																		UpperCamelCase				:		Any				   =			want_ver
																		if op not in ops:
																								raise ValueError(f"""{requirement}: need one of {list(ops.keys()     )}, but got {op}"""     )

    # special case
						if pkg == "python":
												UpperCamelCase				:		Optional[Any]				   =			""".""".join([str(SCREAMING_SNAKE_CASE     ) for x in sys.version_info[:3]]     )
												for op, want_ver in wanted.items():
																		_compare_versions(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
												return

						# check if any version is installed
						try:
												UpperCamelCase				:		Tuple				   =			importlib.metadata.version(SCREAMING_SNAKE_CASE     )
						except importlib.metadata.PackageNotFoundError:
												raise importlib.metadata.PackageNotFoundError(
												    f"""The '{requirement}' distribution was not found and is required by this application. {hint}"""     )

						# check that the right version is installed if version number or a range was provided
						if want_ver is not None:
												for op, want_ver in wanted.items():
																		_compare_versions(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			"""Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
						return require_version(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




